Technical Field
The present invention relates to multitask learning and, more particularly, to sparse multitask regression in multitask learning.
Description of the Related Art
Sparse learning has received a large great deal of interest in high-dimensional data analysis due to its model interpretability and low-computational cost.
One method used in conjunction with sparse learning is the Least Absolute Shrinkage and Selection Operator (LASSO) method. The LASSO method is method of regression analysis in which both regularization and variable selection are performed. This enhances prediction accuracy and interpretability in statistical models.
Among the various techniques, adaptive l1-regularization is an effective framework to improve the convergence behavior of the LASSO, by using varying strengths of regularization across different features. Additionally, the adaptive structure makes it very powerful in modelling grouped sparsity patterns as well, being particularly useful in high-dimensional multitask problems. However, choosing an appropriate, global regularization weight is still an open problem.
In sparse multitask learning, an adaptive LASSO method has been used to solve the problem of joint feature selection in multiple tasks. However, how to determine regularization weights used in the adaptive LASSO algorithm is still an open problem for multitask learning. Currently, the weights are computed based on another, independent estimator, which can lead to a sub-optimal result.
Mixed-norm regularization has been used to solve the sparse multitask learning problem, by enforcing group-wise L1 norm regularization to achieve group-wise feature selection. However, it can only enforce group-wise sparsity, not within-group sparsity.